{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 卷积网络\n",
    "到目前为止，我们已经使用深度全连接网络，使用它们来探索不同的优化策略和网络架构。全连接网络是一个很好的实验平台，因为它们的计算效率非常高，但实际上，所有最先进的结果都使用了卷积网络。\n",
    "首先，您将实现几个在卷积网络中使用的层类型。然后，您将使用这些层在CIFAR-10数据集上训练卷积网络。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.cnn import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
    "from cs231n.layers import *\n",
    "from cs231n.fast_layers import *\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 卷积:简单的前向传递\n",
    "卷积网络的核心是卷积运算。在文件`cs231n/layers`中。在函数`conv_forward_naive`中实现卷积层的前向传递。\n",
    "在这一点上你不必太担心效率;只要用你认为最清楚的方式来写代码。\n",
    "你可以通过运行以下程序来测试你的实现:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_naive\n",
      "difference:  2.2121476417505994e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "w_shape = (3, 3, 4, 4)\n",
    "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
    "b = np.linspace(-0.1, 0.2, num=3)\n",
    "\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "out, _ = conv_forward_naive(x, w, b, conv_param)\n",
    "correct_out = np.array([[[[-0.08759809, -0.10987781],\n",
    "                           [-0.18387192, -0.2109216 ]],\n",
    "                          [[ 0.21027089,  0.21661097],\n",
    "                           [ 0.22847626,  0.23004637]],\n",
    "                          [[ 0.50813986,  0.54309974],\n",
    "                           [ 0.64082444,  0.67101435]]],\n",
    "                         [[[-0.98053589, -1.03143541],\n",
    "                           [-1.19128892, -1.24695841]],\n",
    "                          [[ 0.69108355,  0.66880383],\n",
    "                           [ 0.59480972,  0.56776003]],\n",
    "                          [[ 2.36270298,  2.36904306],\n",
    "                           [ 2.38090835,  2.38247847]]]])\n",
    "\n",
    "# Compare your output to ours; difference should be around 2e-8\n",
    "print('Testing conv_forward_naive')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aside:通过卷积进行图像处理\n",
    "我们将设置一个包含两个图像的输入，并手动设置过滤器来执行常见的图像处理操作(灰度转换和边缘检测)，这是一种既可以检查实现又可以更好地了解卷积层可以执行的操作类型的有趣方法。卷积前向传递将对每个输入图像应用这些操作。然后，我们可以将结果可视化为一个完整性检查。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n",
      "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imread`` instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "D:\\Python\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: DeprecationWarning: `imresize` is deprecated!\n",
      "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``skimage.transform.resize`` instead.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.misc import imread, imresize\n",
    "\n",
    "kitten, puppy = imread('kitten.jpg'), imread('puppy.jpg')\n",
    "# kitten is wide, and puppy is already square\n",
    "d = kitten.shape[1] - kitten.shape[0]\n",
    "kitten_cropped = kitten[:, d//2:-d//2, :]\n",
    "\n",
    "img_size = 200   # Make this smaller if it runs too slow\n",
    "x = np.zeros((2, 3, img_size, img_size))\n",
    "x[0, :, :, :] = imresize(puppy, (img_size, img_size)).transpose((2, 0, 1))\n",
    "x[1, :, :, :] = imresize(kitten_cropped, (img_size, img_size)).transpose((2, 0, 1))\n",
    "\n",
    "# Set up a convolutional weights holding 2 filters, each 3x3\n",
    "w = np.zeros((2, 3, 3, 3))\n",
    "\n",
    "# The first filter converts the image to grayscale.\n",
    "# Set up the red, green, and blue channels of the filter.\n",
    "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
    "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
    "\n",
    "# Second filter detects horizontal edges in the blue channel.\n",
    "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
    "\n",
    "# Vector of biases. We don't need any bias for the grayscale\n",
    "# filter, but for the edge detection filter we want to add 128\n",
    "# to each output so that nothing is negative.\n",
    "b = np.array([0, 128])\n",
    "\n",
    "# Compute the result of convolving each input in x with each filter in w,\n",
    "# offsetting by b, and storing the results in out.\n",
    "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
    "\n",
    "def imshow_noax(img, normalize=True):\n",
    "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
    "    if normalize:\n",
    "        img_max, img_min = np.max(img), np.min(img)\n",
    "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
    "    plt.imshow(img.astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "# Show the original images and the results of the conv operation\n",
    "plt.subplot(2, 3, 1)\n",
    "imshow_noax(puppy, normalize=False)\n",
    "plt.title('Original image')\n",
    "plt.subplot(2, 3, 2)\n",
    "imshow_noax(out[0, 0])\n",
    "plt.title('Grayscale')\n",
    "plt.subplot(2, 3, 3)\n",
    "imshow_noax(out[0, 1])\n",
    "plt.title('Edges')\n",
    "plt.subplot(2, 3, 4)\n",
    "imshow_noax(kitten_cropped, normalize=False)\n",
    "plt.subplot(2, 3, 5)\n",
    "imshow_noax(out[1, 0])\n",
    "plt.subplot(2, 3, 6)\n",
    "imshow_noax(out[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 卷积:简单的后向传递\n",
    "在文件`cs231n/layers.py`中的函数`conv_backward_naive`中实现卷积操作的向后传递。同样，你不需要太担心计算效率。\n",
    "完成后，运行以下命令，用数值梯度检查检查后向遍历。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_backward_naive function\n",
      "dx error:  1.159803161159293e-08\n",
      "dw error:  2.247109434939654e-10\n",
      "db error:  3.37264006649648e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(4, 3, 5, 5)\n",
    "w = np.random.randn(2, 3, 3, 3)\n",
    "b = np.random.randn(2,)\n",
    "dout = np.random.randn(4, 2, 5, 5)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "out, cache = conv_forward_naive(x, w, b, conv_param)\n",
    "dx, dw, db = conv_backward_naive(dout, cache)\n",
    "\n",
    "# Your errors should be around 1e-8'\n",
    "print('Testing conv_backward_naive function')\n",
    "print('dx error: ', rel_error(dx, dx_num))\n",
    "print('dw error: ', rel_error(dw, dw_num))\n",
    "print('db error: ', rel_error(db, db_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 最大池化:简单的前向\n",
    "在文件`cs231n/layers.py`中的函数`max_pool_forward_naive`中实现max-pooling操作的前向传递。同样，不要太担心计算效率。\n",
    "检查你的实现运行以下:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_forward_naive function:\n",
      "difference:  4.1666665157267834e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
    "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
    "\n",
    "out, _ = max_pool_forward_naive(x, pool_param)\n",
    "\n",
    "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
    "                          [-0.20421053, -0.18947368]],\n",
    "                         [[-0.14526316, -0.13052632],\n",
    "                          [-0.08631579, -0.07157895]],\n",
    "                         [[-0.02736842, -0.01263158],\n",
    "                          [ 0.03157895,  0.04631579]]],\n",
    "                        [[[ 0.09052632,  0.10526316],\n",
    "                          [ 0.14947368,  0.16421053]],\n",
    "                         [[ 0.20842105,  0.22315789],\n",
    "                          [ 0.26736842,  0.28210526]],\n",
    "                         [[ 0.32631579,  0.34105263],\n",
    "                          [ 0.38526316,  0.4       ]]]])\n",
    "\n",
    "# Compare your output with ours. Difference should be around 1e-8.\n",
    "print('Testing max_pool_forward_naive function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 最大池化:简单后向传递\n",
    "在文件`cs231n/layers.py`中的函数`max_pool_backward_naive`中实现最大池操作的向后传递。你不需要担心计算效率。\n",
    "检查你的实现与数值梯度检查运行以下:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_backward_naive function:\n",
      "dx error:  3.27562514223145e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(3, 2, 8, 8)\n",
    "dout = np.random.randn(3, 2, 4, 4)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
    "\n",
    "out, cache = max_pool_forward_naive(x, pool_param)\n",
    "dx = max_pool_backward_naive(dout, cache)\n",
    "\n",
    "# Your error should be around 1e-12\n",
    "print('Testing max_pool_backward_naive function:')\n",
    "print('dx error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 快速层\n",
    "快速实现卷积和池化层具有挑战性。为了避免麻烦，我们在文件`cs231n/fast_layers.py`中为卷积和池化层提供了向前和向后传递的快速实现。\n",
    "快速卷积的实现依赖于一个Cython扩展;要编译它，你需要运行以下`cs231n `目录:\n",
    "\n",
    "```bash\n",
    "python setup.py build_ext --inplace\n",
    "```\n",
    "卷积和池化层的快速版本的API与上面实现的原始版本完全相同:前向传递接收数据、权重和参数，并产生输出和缓存对象;后向传递接收上游导数和缓存对象，并产生与数据和权重相关的梯度。\n",
    "\n",
    "**注:** 池的快速实现只有在池区域不重叠和平铺输入时才会优化执行。如果不满足这些条件，那么快速池实现不会比简单实现快很多。\n",
    "\n",
    "你可以比较这些层的性能简单和快速版本运行以下:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_fast:\n",
      "Naive: 0.200464s\n",
      "Fast: 0.019946s\n",
      "Speedup: 10.050167x\n",
      "Difference:  4.926407851494105e-11\n",
      "\n",
      "Testing conv_backward_fast:\n",
      "Naive: 0.914554s\n",
      "Fast: 0.016954s\n",
      "Speedup: 53.941923x\n",
      "dx difference:  1.006330206620509e-11\n",
      "dw difference:  2.497142522392147e-13\n",
      "db difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
    "from time import time\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 31, 31)\n",
    "w = np.random.randn(25, 3, 3, 3)\n",
    "b = np.random.randn(25,)\n",
    "dout = np.random.randn(100, 25, 16, 16)\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing conv_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('Difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting conv_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))\n",
    "print('dw difference: ', rel_error(dw_naive, dw_fast))\n",
    "print('db difference: ', rel_error(db_naive, db_fast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pool_forward_fast:\n",
      "Naive: 0.010943s\n",
      "fast: 0.005981s\n",
      "speedup: 1.829513x\n",
      "difference:  0.0\n",
      "\n",
      "Testing pool_backward_fast:\n",
      "Naive: 0.022975s\n",
      "speedup: 1.441425x\n",
      "dx difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 32, 32)\n",
    "dout = np.random.randn(100, 3, 16, 16)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing pool_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting pool_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 卷积的“三明治”层\n",
    "前面我们介绍了“三明治”层的概念，它将多个操作组合成常用的模式。在文件`cs231n/layer_utils`中。你会发现三明治层实现了一些常用的卷积网络模式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu_pool\n",
      "dx error:  5.828178746516271e-09\n",
      "dw error:  8.443628091870788e-09\n",
      "db error:  3.57960501324485e-10\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 16, 16)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
    "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
    "\n",
    "print('Testing conv_relu_pool')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu:\n",
      "dx error:  3.5600610115232832e-09\n",
      "dw error:  2.2497700915729298e-10\n",
      "db error:  1.3087619975802167e-10\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 8, 8)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
    "dx, dw, db = conv_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "print('Testing conv_relu:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三层卷积网络\n",
    "现在您已经实现了所有必要的层，我们可以将它们放到一个简单的卷积网络中。\n",
    "打开文件`cs231n/classifiers/cnn`。完成`ThreeLayerConvNet`类的实现。运行以下单元来帮助您调试:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查损失\n",
    "在你建立一个新的网络之后，你应该做的第一件事就是检查损失。当我们使用softmax损失时，我们期望随机权重的损失(没有正则化)是关于“log(C)”的“C”类。当我们添加正则化时，这个应该会增加。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial loss (no regularization):  2.302586071243987\n",
      "Initial loss (with regularization):  2.508255638232932\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet()\n",
    "\n",
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (no regularization): ', loss)\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (with regularization): ', loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 梯度检查\n",
    "在损失看起来合理之后，使用数值梯度检查来确保您的后向传递是正确的。当你使用数值梯度检查时，你应该在每一层使用少量的人工数据和少量的神经元。注意:正确的实现在1e-2之前仍然可能有相对错误。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 1.380104e-04\n",
      "W2 max relative error: 1.822723e-02\n",
      "W3 max relative error: 3.064049e-04\n",
      "b1 max relative error: 3.477652e-05\n",
      "b2 max relative error: 2.516375e-03\n",
      "b3 max relative error: 7.945660e-10\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.0\n",
    "num_classes = 10\n",
    "np.random.seed(231)\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n",
    "                          input_dim=input_dim, hidden_dim=7,\n",
    "                          dtype=np.float64)\n",
    "loss, grads = model.loss(X, y)\n",
    "for param_name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 小数据过拟合\n",
    "一个不错的技巧是使用一些训练样本来训练您的模型。您应该能够过度适合小的数据集，这将导致非常高的训练精度和相对较低的验证精度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 30) loss: 2.414060\n",
      "(Epoch 0 / 15) train acc: 0.200000; val_acc: 0.128000\n",
      "(Iteration 2 / 30) loss: 2.231915\n",
      "(Epoch 1 / 15) train acc: 0.290000; val_acc: 0.145000\n",
      "(Iteration 3 / 30) loss: 1.935038\n",
      "(Iteration 4 / 30) loss: 1.795171\n",
      "(Epoch 2 / 15) train acc: 0.420000; val_acc: 0.144000\n",
      "(Iteration 5 / 30) loss: 1.479354\n",
      "(Iteration 6 / 30) loss: 1.711093\n",
      "(Epoch 3 / 15) train acc: 0.490000; val_acc: 0.178000\n",
      "(Iteration 7 / 30) loss: 1.700716\n",
      "(Iteration 8 / 30) loss: 1.406703\n",
      "(Epoch 4 / 15) train acc: 0.710000; val_acc: 0.199000\n",
      "(Iteration 9 / 30) loss: 0.885724\n",
      "(Iteration 10 / 30) loss: 0.963994\n",
      "(Epoch 5 / 15) train acc: 0.730000; val_acc: 0.212000\n",
      "(Iteration 11 / 30) loss: 0.750023\n",
      "(Iteration 12 / 30) loss: 0.642634\n",
      "(Epoch 6 / 15) train acc: 0.830000; val_acc: 0.204000\n",
      "(Iteration 13 / 30) loss: 0.697060\n",
      "(Iteration 14 / 30) loss: 0.430327\n",
      "(Epoch 7 / 15) train acc: 0.890000; val_acc: 0.217000\n",
      "(Iteration 15 / 30) loss: 0.299633\n",
      "(Iteration 16 / 30) loss: 0.416626\n",
      "(Epoch 8 / 15) train acc: 0.870000; val_acc: 0.208000\n",
      "(Iteration 17 / 30) loss: 0.744293\n",
      "(Iteration 18 / 30) loss: 0.155720\n",
      "(Epoch 9 / 15) train acc: 0.940000; val_acc: 0.193000\n",
      "(Iteration 19 / 30) loss: 0.175586\n",
      "(Iteration 20 / 30) loss: 0.329272\n",
      "(Epoch 10 / 15) train acc: 0.920000; val_acc: 0.190000\n",
      "(Iteration 21 / 30) loss: 0.346701\n",
      "(Iteration 22 / 30) loss: 0.076882\n",
      "(Epoch 11 / 15) train acc: 0.990000; val_acc: 0.210000\n",
      "(Iteration 23 / 30) loss: 0.111272\n",
      "(Iteration 24 / 30) loss: 0.139852\n",
      "(Epoch 12 / 15) train acc: 0.970000; val_acc: 0.201000\n",
      "(Iteration 25 / 30) loss: 0.070718\n",
      "(Iteration 26 / 30) loss: 0.092726\n",
      "(Epoch 13 / 15) train acc: 0.980000; val_acc: 0.212000\n",
      "(Iteration 27 / 30) loss: 0.070167\n",
      "(Iteration 28 / 30) loss: 0.046357\n",
      "(Epoch 14 / 15) train acc: 1.000000; val_acc: 0.212000\n",
      "(Iteration 29 / 30) loss: 0.028140\n",
      "(Iteration 30 / 30) loss: 0.074156\n",
      "(Epoch 15 / 15) train acc: 1.000000; val_acc: 0.192000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=15, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-4,\n",
    "                },\n",
    "                verbose=True, print_every=1)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制损失、训练精度和验证精度应该显示清楚的过拟合:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练网络\n",
    "通过对三层卷积网络进行1个epoch的训练，训练集的准确率应达到40%以上:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 980) loss: 2.304740\n",
      "(Epoch 0 / 1) train acc: 0.103000; val_acc: 0.107000\n",
      "(Iteration 21 / 980) loss: 2.112254\n",
      "(Iteration 41 / 980) loss: 1.839801\n",
      "(Iteration 61 / 980) loss: 1.654228\n",
      "(Iteration 81 / 980) loss: 1.710945\n",
      "(Iteration 101 / 980) loss: 1.712855\n",
      "(Iteration 121 / 980) loss: 1.598298\n",
      "(Iteration 141 / 980) loss: 1.689535\n",
      "(Iteration 161 / 980) loss: 1.683083\n",
      "(Iteration 181 / 980) loss: 1.606298\n",
      "(Iteration 201 / 980) loss: 1.914768\n",
      "(Iteration 221 / 980) loss: 1.693683\n",
      "(Iteration 241 / 980) loss: 1.484315\n",
      "(Iteration 261 / 980) loss: 1.474231\n",
      "(Iteration 281 / 980) loss: 1.592995\n",
      "(Iteration 301 / 980) loss: 1.400443\n",
      "(Iteration 321 / 980) loss: 1.547121\n",
      "(Iteration 341 / 980) loss: 1.421683\n",
      "(Iteration 361 / 980) loss: 1.577219\n",
      "(Iteration 381 / 980) loss: 1.195093\n",
      "(Iteration 401 / 980) loss: 1.530069\n",
      "(Iteration 421 / 980) loss: 1.245582\n",
      "(Iteration 441 / 980) loss: 1.456997\n",
      "(Iteration 461 / 980) loss: 1.585542\n",
      "(Iteration 481 / 980) loss: 1.296335\n",
      "(Iteration 501 / 980) loss: 1.147808\n",
      "(Iteration 521 / 980) loss: 1.496570\n",
      "(Iteration 541 / 980) loss: 1.335086\n",
      "(Iteration 561 / 980) loss: 1.412038\n",
      "(Iteration 581 / 980) loss: 1.131724\n",
      "(Iteration 601 / 980) loss: 1.265873\n",
      "(Iteration 621 / 980) loss: 1.314748\n",
      "(Iteration 641 / 980) loss: 1.347711\n",
      "(Iteration 661 / 980) loss: 1.211517\n",
      "(Iteration 681 / 980) loss: 1.594141\n",
      "(Iteration 701 / 980) loss: 1.212646\n",
      "(Iteration 721 / 980) loss: 1.322396\n",
      "(Iteration 741 / 980) loss: 1.449204\n",
      "(Iteration 761 / 980) loss: 1.341910\n",
      "(Iteration 781 / 980) loss: 1.576904\n",
      "(Iteration 801 / 980) loss: 1.440206\n",
      "(Iteration 821 / 980) loss: 1.262728\n",
      "(Iteration 841 / 980) loss: 1.164722\n",
      "(Iteration 861 / 980) loss: 1.470526\n",
      "(Iteration 881 / 980) loss: 1.317708\n",
      "(Iteration 901 / 980) loss: 1.164740\n",
      "(Iteration 921 / 980) loss: 1.348533\n",
      "(Iteration 941 / 980) loss: 1.317858\n",
      "(Iteration 961 / 980) loss: 1.275114\n",
      "(Epoch 1 / 1) train acc: 0.579000; val_acc: 0.542000\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                num_epochs=1, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-4,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可视化过滤器\n",
    "你可以从训练好的网络中可视化第一层卷积滤波器，方法如下:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 空间批处理标准化\n",
    "我们已经看到，批处理规范化对于训练深度全连接网络是一种非常有用的技术。批处理标准化也可以用于卷积网络，但是我们需要稍微调整一下;修改将被称为“空间批处理规范化”。\n",
    "\n",
    "通常，批处理规范化接受shape`(N, D)`的输入，并生成shape`(N, D)`的输出，其中我们对整个minibatch维`N`进行规范化。对于来自卷积层的数据，批处理归一化需要接收shape`(N, C, H, W)`的输入，生成shape`(N, C, H, W)`的输出，其中`N`维给出的是minibatch size,`(H, W)`维给出的是feature map的空间大小。\n",
    "\n",
    "如果feature map是使用convolutions来生成的，那么我们希望每个feature channel的统计在不同的image之间，以及在同一image内的不同位置上，是相对一致的。因此，空间批处理标准化通过计算minibatch维度`N`和空间维度`H`和`W`上的统计信息来计算每个`C`特征通道的平均值和方差。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 空间批处理规范化:前向\n",
    "在文件`cs231n/layers`中。在函数`spatial_batchnorm_forward`中实现空间批处理规范化的前向传递。检查你的实现运行以下:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [9.33463814 8.90909116 9.11056338]\n",
      "  Stds:  [3.61447857 3.19347686 3.5168142 ]\n",
      "After spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ 6.18949336e-16  5.99520433e-16 -1.22124533e-16]\n",
      "  Stds:  [0.99999962 0.99999951 0.9999996 ]\n",
      "After spatial batch normalization (nontrivial gamma, beta):\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [6. 7. 8.]\n",
      "  Stds:  [2.99999885 3.99999804 4.99999798]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization\n",
    "\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "\n",
    "print('Before spatial batch normalization:')\n",
    "print('  Shape: ', x.shape)\n",
    "print('  Means: ', x.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', x.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones(C), np.zeros(C)\n",
    "bn_param = {'mode': 'train'}\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization:')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to beta and stds close to gamma\n",
    "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization (nontrivial gamma, beta):')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After spatial batch normalization (test-time):\n",
      "  means:  [-0.08034406  0.07562881  0.05716371  0.04378383]\n",
      "  stds:  [0.96718744 1.0299714  1.02887624 1.00585577]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "N, C, H, W = 10, 4, 11, 12\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(C)\n",
    "beta = np.zeros(C)\n",
    "for t in range(50):\n",
    "  x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After spatial batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', a_norm.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 空间批处理规范化:后向\n",
    "在文件`cs231n/layers`中在函数`spatial_batchnorm_back`中实现空间批处理规范化的后向遍历。运行以下程序来检查你的实现使用数值梯度检查:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  2.786648197756335e-07\n",
      "dgamma error:  7.0974817113608705e-12\n",
      "dbeta error:  3.275608725278405e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(C)\n",
    "beta = np.random.randn(C)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 额外信用描述\n",
    "如果您为了获得额外的学分而实现了任何其他特性，请在这里用指向此文件或其他文件(如果适用)中的任何代码的指针清楚地描述它们。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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